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A paradigm for customer-driven product design approach using extended axiomatic design

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Abstract

In the present times, due to increase in customer demands, products complexity is on the rise. This calls for the designers to strike a balance between a wide range of design alternatives and a large set of conflicting criteria. Hence, to take a sound decision by identifying a viable combination of customer requirements and satisfy the conflicting requirements is a difficult task for both the designer and the manufacturer. This work extends the axiomatic design theory to align the customer requirements (CRs) and design parameters (DPs) and generates multiple possible design alternatives based on the weightages of analytic hierarchy process (AHP). Such design alternatives are evaluated on the basis of their overall performance in line with the expected customer attributes, and the best design is identified by integrating the technique for order of preference by similarity to ideal solution, a ranking multi-criteria decision-making method, with AHP. This work unfolds a support tool for decision makers to accurately and effectively select CRs by a useful aggregation of function requirements and DPs. An industrial example is produced to demonstrate the applicability of the proposed method. This intelligent decision-making method is useful from the customers as well as the manufacturers’ perspective.

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References

  • Albayrak, E., & Erensal, Y. C. (2004). Using analytic hierarchy process (AHP) to improve human performance: An application of multiple criteria decision making problem. Journal of Intelligent Manufacturing, 15(4), 491–503.

    Article  Google Scholar 

  • Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.

    Article  Google Scholar 

  • Bhutia, P. W., & Phipon, R. (2012). Application of ahp and topsis method for supplier selection problem. IOSR Journal of Engineering (IOSRJEN), 2, 43–50.

    Article  Google Scholar 

  • Cui, A. S., & Wu, F. (2015). Utilizing customer knowledge in innovation: Antecedents and impact of customer involvement on new product performance. Journal of the Academy of Marketing Science, 44, 1–23.

    Google Scholar 

  • Dağdeviren, M. (2008). Decision making in equipment selection: An integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing, 19(4), 397–406.

    Article  Google Scholar 

  • Do, S. H., & Park, G. J. (2001). Application of design axioms for glass bulb design and software development for design automation. Journal of Mechanical Design, 123(3), 322–329.

    Article  Google Scholar 

  • Gu, P., Rao, H. A., & Tseng, M. M. (2001). Systematic design of manufacturing systems based on axiomatic design approach. CIRP Annals-Manufacturing Technology, 50(1), 299–304.

  • Guan, X., Wang, Y., & Tao, L. (2009). Machining scheme selection of digital manufacturing based on genetic algorithm and AHP. Journal of Intelligent Manufacturing, 20(6), 661–669.

    Article  Google Scholar 

  • Han, W. J., & Tsay, W. D. (1998). Formulation of quality strategy using analytic hierarchy process. In Twenty seven annual meeting of the western decision science institute, University of Northern Colorado, USA, pp. 580–583.

  • Harding, J. A., Popplewell, K., Fung, R. Y., & Omar, A. R. (2001). An intelligent information framework relating customer requirements and product characteristics. Computers in Industry, 44(1), 51–65.

    Article  Google Scholar 

  • Hirani, H., & Suh, N. P. (2005). Journal bearing design using multiobjective genetic algorithm and axiomatic design approaches. Tribology International, 38(5), 481–491.

    Article  Google Scholar 

  • Hua Lu, M., Madu, C. N., Kuei, C. H., & Winokur, D. (1994). Integrating QFD, AHP and benchmarking in strategic marketing. Journal of Business and Industrial Marketing, 9(1), 41–50.

    Article  Google Scholar 

  • Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (Vol. 186, pp. 58–191). Springer, Berlin.

  • Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194–209.

    Article  Google Scholar 

  • Kim, G., Park, C. S., & Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, 50(1), 23–33.

    Article  Google Scholar 

  • Kremer, G. O., Chiu, M. C., Lin, C. Y., Gupta, S., Claudio, D., & Thevenot, H. (2012). Application of axiomatic design, TRIZ, and mixed integer programming to develop innovative designs: A locomotive ballast arrangement case study. The International Journal of Advanced Manufacturing Technology, 61(5–8), 827–842.

    Article  Google Scholar 

  • Krishnapillai, R., & Zeid, A. (2006). Mapping product design specification for mass customization. Journal of Intelligent Manufacturing, 17(1), 29–43.

    Article  Google Scholar 

  • Kumar, A., Jain, V., & Kumar, S. (2014). A comprehensive environment friendly approach for supplier selection. Omega, 42(1), 109–123.

    Article  Google Scholar 

  • Kusiak, A. (1999). Engineering design: Products, processes, and systems. San Diego, CA: Academic Press.

  • Lin, M. C., Wang, C. C., Chen, M. S., & Chang, C. A. (2008). Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, 59(1), 17–31.

    Article  Google Scholar 

  • Park, K., Kang, B., Song, K., & Park, G. (2003). Design of a spacer grid using axiomatic design. Journal of Nuclear Science and Technology, 40(12), 989–997.

    Article  Google Scholar 

  • Saaty, L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

    Google Scholar 

  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.

    Article  Google Scholar 

  • Sharma, S., & Balan, S. (2013). An integrative supplier selection model using Taguchi loss function, TOPSIS and multi criteria goal programming. Journal of Intelligent Manufacturing, 24(6), 1123–1130.

    Article  Google Scholar 

  • Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7), 801–813.

    Article  Google Scholar 

  • Shirwaiker, R. A., & Okudan, G. E. (2008). Triz and axiomatic design: A review of case-studies and a proposed synergistic use. Journal of Intelligent Manufacturing, 19(1), 33–47.

    Article  Google Scholar 

  • Suh, N. P. (1990). The principles of design (Vol. 990). New York: Oxford University Press.

    Google Scholar 

  • Suh, N. P. (2005). Complexity: Theory and applications. New York: Oxford University Press on Demand.

    Google Scholar 

  • Wang, X., Chan, H. K., Lee, C. K., & Li, D. (2015). A hierarchical model for eco-design of consumer electronic products. Technological and Economic Development of Economy, 21(1), 48–64.

    Article  Google Scholar 

  • Yang, K., & Zhang, H. (2000). Compatibility analysis and case studies of axiomatic design and TRIZ. The TRIZ Journal. http://www.triz-journal.com.

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Correspondence to Puneet Tandon.

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Kumar, P., Tandon, P. A paradigm for customer-driven product design approach using extended axiomatic design. J Intell Manuf 30, 589–603 (2019). https://doi.org/10.1007/s10845-016-1266-2

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  • DOI: https://doi.org/10.1007/s10845-016-1266-2

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